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Enhancing Vision-Language Model with Unmasked Token Alignment (2405.19009v2)

Published 29 May 2024 in cs.CV

Abstract: Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstrated remarkable performance, training it from scratch on noisy web-scale datasets is computationally demanding. On the other hand, mask-then-predict pre-training approaches, like Masked Image Modeling (MIM), offer efficient self-supervised learning for single-modal representations. This paper introduces Unmasked Token Alignment (UTA), a method that leverages existing CLIP models to further enhance its vision-language representations. UTA trains a Vision Transformer (ViT) by aligning unmasked visual tokens to the corresponding image tokens from a frozen CLIP vision encoder, which automatically aligns the ViT model with the CLIP text encoder. The pre-trained ViT can be directly applied for zero-shot evaluation even without training on image-text pairs. Compared to MIM approaches, UTA does not suffer from training-finetuning inconsistency and is much more training-efficient by avoiding using the extra [MASK] tokens. Extensive experimental results demonstrate that UTA can enhance CLIP models and outperform existing MIM methods on various uni- and multi-modal benchmarks. Code and models are available at https://github.com/jihaonew/UTA.

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Authors (5)
  1. Jihao Liu (60 papers)
  2. Jinliang Zheng (10 papers)
  3. Boxiao Liu (16 papers)
  4. Yu Liu (784 papers)
  5. Hongsheng Li (340 papers)
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GitHub

  1. GitHub - jihaonew/UTA: Enhancing Vision-Language Model with Unmasked Token Alignment (TMLR) (9 stars)